Agriculture is an intrinsically site-specific activity. Location determines the available physical assets, climatic patterns, and accessible input and output markets which ultimately influence the choice of production inputs and outputs. Data on agricultural production (i.e., harvested area, production quantity and yield) is usually representative of and reported at national and sub-national geo-political boundaries, but these statistics do not give any indication of the diversity and spatial patterns in agricultural production. It is increasingly common for agricultural and environmental studies to rely on the use of gridded crop production data generated by the downscaling of crop production statistics originally reported by more geographically-aggregated administrative units.
The drive for improved spatial resolution of the location (area) and performance (yield) of crop production is fueled by a number of mutually reinforcing factors. First and foremost, is growing awareness that a major obstacle to improving the effectiveness of policies and interventions aimed at improving rural well-being, agricultural growth, and natural resource sustainability is our inability to adequately account for the spatial heterogeneity of socio-economic, production, and environmental conditions. The more reliably we can assess the spatial distribution and covariance of such factors, the more cost-effective can be the formulation and targeting of appropriate policy and investment actions. Second, is the growing interest in understanding spatial patterns of agricultural production that might reveal untapped opportunities in, say, intensification and diversification, regional marketing, processing and trade or that might uncover significant levels of regional inequality and that, furthermore, might be helpful in shaping spatially-explicit strategic responses to such opportunities and challenges. Third, is simply the increasing ease and lower costs of exploring the spatial dimensions of agricultural development. Our capacity to acquire, manage, and share geo-referenced data has expanded significantly over the past twenty years, as have the range and utility of satellite and communications products and services—including the cropland and irrigated area land cover products utilized extensively in crop production downscaling efforts such as those described here.
There have been some previous efforts to generate global crop maps. Leff, Ramankutty and Foley (2004) synthesized satellite-derived land cover data and agricultural census data to produce global data sets of the distribution of 18 major crops across the world. They first collected agricultural census data on harvested area for crops at (mostly) national or sub-national level. From these data, for each administrative unit, they estimated the proportion of each of the 18 major crops to the total harvested area. After masking non-cropland areas and applying a smoothing algorithm to correct abrupt and arbitrary changes across administrative boundaries, Leff, Ramankutty and Foley (2004) multiplied the resulting data of individual crop proportions by the cropland data sets to obtain the per-pixel proportion of each of the 18 major crops. Monfreda, Ramankutty and Foley (2008) used pixel-level cropland area shares as uniform weights for all crops and produced the year 2000 area (harvested) and yield distribution of 175 distinct crops of the world. By combining Ramankutty et al. (2008), Monfreda, Ramankutty and Foley (2008) and the global map of irrigation areas (GMIA), Portmann et al (2010) produced a global dataset of monthly growing areas of 26 irrigated crops on the same 5×5 arc minutes grid. You et al (2014) produced global crop maps through a downscaling approach that accounts for spatial variation in the biophysical conditions influencing the productivity of individual crops within the cropland extent, and that uses crop prices to weigh the gross revenue potential of alternate crops when considering how to prioritize the allocation of specific crops to individual gridcells. While all the previous modelling work has admitted the huge uncertainty of downscaling the crop production, none of them have explicitly included uncertainty/error term in their approaches. Therefore what is needed is the new, improved method to explicitly include error term to tackle the inherent uncertainty in the downscaling modelling.